前沿预测：2025年快照

Snapshot 2025Projectionsat theFrontier Table ofContents Authors0303 Autonomous Experimentation2601 Virtual Cell0605 Reproductive Health5807 Plant Engineering74Introduction0404 Mood & Behaviour4702 Data Infrastructure1706 Synthetic Biology6608 Diagnostics & Biomarkers83 Authors Virtual CellDaniel Burkhardt Virtual CellAnthony Costa Sasha Eremina Zahra Khwaja Oliver Hernandez Amee Kapadia Plant Engineering Plant Engineering Mood & Behaviour Reproductive Health Shelby Newsad Virtual CellVega Shah Mikaela Kimpton Bogdan Knezevic Charlene Tang Isabel Zhang Reproductive Health Diagnostics & Biomarkers Autonomous Experimentation Synthetic Biology Data Infrastructure Introduction AI is no longer a tool in the background — it is everywhere, increasinglyautomating not only our tasks but also functions that once demand-ed human thought and decision-making. As more of what we used tomake, analyze, and even imagine becomes automated, the danger isclear: a world where creation itself is outsourced. That is why human creativity and curiosity matter more than ever.Breakthroughs will not come from recycling what already exists online,but from imagining futures that don’t yet exist — and charting the pathsto reach them. The signal from the noise will emerge through care-ful imagining, disciplined planning, and the courage to explore whatothers haven’t yet dared. AI-BasedVirtual Cell Models DriveDiscovery&Pre-Clinical Testing Autonomous Experimentation:The Future of Biological Discovery Each year, the Decoding Bio community produces aSnapshotof tech-nology’s present and its emerging themes. This year’s edition takes adifferent shape: rather than serving only as a retrospective, it becomesa forward-looking exercise in imagination. Each theme is cast as a pro-jection toward 2030, anchored in the realities of 2025. By 2030, virtual cells, digital twins of human cells, could revolutionizethe way we do biology. If current efforts are successful, instead of di-rectly testing every idea in the lab, researchers could first use thesevirtual cells to simulate different scenarios and focus on the ones mostlikely to lead to fundamental discoveries and safer therapeutics. Thesemodels would mimic the behavior of real cells, helping to speed up bothbasic research and the development of new treatments. Scientists havebeen working toward building a virtual cell since the early 2000s, butinitial efforts have been focused on single-celled microbes. The combi-nation of massive multi-omics datasets and a golden era of molecularmodeling tools is finally making it possible to build something that livesup to the promise of simulating human biology. By 2030, fully integrated robotics, hardware, and software will auton-omously design, execute, and analyze experiments across modalitiesand cellular systems, seamlessly linking research labs to pharma work-flows, transforming discovery, scale-up, and manufacturing into a con-tinuous, data-driven process that operates 24/7/365 without humanbottlenecks. This represents a fundamental shift in the scientific meth-od, from episodic human-driven testing to continuous, data-driven ex-ploration, exemplified by initiatives like the King Lab’s “Robot Scientists”Adam, Eve, and the upcoming Genesis system, and companies like LilaSciences and Intrepid Labs which are building AI-powered autonomouslabs. In doing so, we consider not just where we stand today, but also thefutures we hope to shape. We ask what 2030 could look like if currenttrajectories continue — or if new ideas take hold. We reflect on the stateof 2025, identifying what must change to close the distance betweenwhere we are and where we want to be. We confront the challengesthat will define the journey, and we highlight the labs, companies, andcommunities already pioneering the way forward. The result is a snapshot that is both a record of the present and a mapof possibility — a scaffold for thinking, planning, and shaping the nextdecade of biology and technology. Plant Engineering for Consumer-Tailored Food and SustainableMaterials Proactive, Predictive,and Person-alized Diagnostics&Biomarkers UnifiedData Infrastructure inBiotech By 2030, individuals will have affordable, real-time access to their bio-marker profiles and actionable insights, shifting healthcare toward ear-lier intervention and tighter disease management loops. This is drivenby a market shift where consumers are increasingly demanding own-ership of their health data, leading to the rise of integrated, multi-mod-al platforms that combine advanced diagnostics with wearables andAI-driven insights for predictive, preventative, personalized, and partic-ipatory healthcare. By 2030, biotech companies will leverage unified data infrastructureto gain unprecedented real-time R&D awareness, informing every ma-jor decision and rapidly cutting R&D costs and time-to-market. Thistransformation will allow scientific leaders to tap into their “data ocean”and leverage